AI Medical Compendium Topic:
Electronic Health Records

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Multi-domain clinical natural language processing with MedCAT: The Medical Concept Annotation Toolkit.

Artificial intelligence in medicine
Electronic health records (EHR) contain large volumes of unstructured text, requiring the application of information extraction (IE) technologies to enable clinical analysis. We present the open source Medical Concept Annotation Toolkit (MedCAT) that...

The quest for better clinical word vectors: Ontology based and lexical vector augmentation versus clinical contextual embeddings.

Computers in biology and medicine
BACKGROUND: Word vectors or word embeddings are n-dimensional representations of words and form the backbone of Natural Language Processing of textual data. This research experiments with algorithms that augment word vectors with lexical constraints ...

Machine learning and deep learning to predict mortality in patients with spontaneous coronary artery dissection.

Scientific reports
Machine learning (ML) and deep learning (DL) can successfully predict high prevalence events in very large databases (big data), but the value of this methodology for risk prediction in smaller cohorts with uncommon diseases and infrequent events is ...

Contextual embedding bootstrapped neural network for medical information extraction of coronary artery disease records.

Medical & biological engineering & computing
Coronary artery disease (CAD) is the major cause of human death worldwide. The development of new CAD early diagnosis methods based on medical big data has a great potential to reduce the risk of CAD death. In this process, neural network (NN), as a ...

A scalable approach for developing clinical risk prediction applications in different hospitals.

Journal of biomedical informatics
OBJECTIVE: Machine learning (ML) algorithms are now widely used in predicting acute events for clinical applications. While most of such prediction applications are developed to predict the risk of a particular acute event at one hospital, few effort...

Three ways of knowing: the integration of clinical expertise, evidence-based medicine, and artificial intelligence in assisted reproductive technologies.

Journal of assisted reproduction and genetics
Decision-making in fertility care is on the cusp of a significant frameshift. Online tools to integrate artificial intelligence into the decision-making process across all aspects of ART are rapidly emerging. These tools have the potential to improve...

A neuralized feature engineering method for entity relation extraction.

Neural networks : the official journal of the International Neural Network Society
Making full use of semantic and structure information in a sentence is critical to support entity relation extraction. Neural networks use stacked neural layers to perform designated feature transformations and can automatically extract high-order ab...

Review of Temporal Reasoning in the Clinical Domain for Timeline Extraction: Where we are and where we need to be.

Journal of biomedical informatics
Understanding a patient's medical history, such as how long symptoms last or when a procedure was performed, is vital to diagnosing problems and providing good care. Frequently, important information regarding a patient's medical timeline is buried i...

Core services that power AI-driven transformation in cancer research and care.

Biochimica et biophysica acta. Reviews on cancer
This review captures some key lessons learned in the course of helping some of America's leading healthcare AI innovators achieve scale and sustained impact in complex research and care delivery ecosystems. AI innovators may find it useful to access ...

Evaluating eligibility criteria of oncology trials using real-world data and AI.

Nature
There is a growing focus on making clinical trials more inclusive but the design of trial eligibility criteria remains challenging. Here we systematically evaluate the effect of different eligibility criteria on cancer trial populations and outcomes ...